1. Technical Field
The present invention generally relates to distributed networks, and particularly relates to a system and method for deploying and managing intelligent nodes in a distributed network, such as may be used to extend enterprise and IP network infrastructure to processing data from sensors, actuators, and other “edge” systems and devices.
2. Background
Enterprise networks traditionally work well at linking together different workgroups within organizations and making business applications and data available across those workgroups. However, significant challenges arise when extending enterprise network connectivity via devices and systems that operate at the network edges and provide physical world data collection and control processing. For example, integrating diverse data from edge devices and systems into enterprise networks often requires developing customized software code and “middleware” components, to provide for data input/output, formatting, conversion, translation, and communications.
The time and expertise needed to develop such software/middleware is prohibitive. Some companies skip the expense altogether and suffer from the attendant inefficiencies arising from having critical assets—sensors, actuators, and other edge systems and devices—stranded outside of their enterprise networks. Other companies bear the expense and yet get less business value from the effort than they expected, because of limitations, complexity and errors in the custom-developed integration software. By its very nature, software of that type usually is difficult and expensive to debug and maintain. Worse still, the complex, custom nature of such software clouds upgrade paths, meaning that such software may not be readily extended to new devices and systems.
As a further issue, the convergence of sensors, actuators, and other edge systems and devices into enterprise networks can overwhelm the bandwidth of networks, especially wireless networks. In addition, the sheer volume of data inflowing from edge systems and devices can overwhelm the end-users of that data. Consequently, potentially sophisticated data filtering and event detection algorithms may be needed, further raising the customization requirements and development complexity of the integration software. In many instances, one might fairly argue that acquiring data represents the easy part of building distributed, intelligent networks, and that the greater challenge lies in efficiently processing and sharing that data.